aion

Time Series models for keras in R

Installation

You can install the development version of aion from GitHub with:

# install.packages("devtools")
devtools::install_github("krzjoa/aion")

Key features

• Temporal Fusion Transformer model
• additional layers: Temporal Convolutional Network block & Legendre Memory Unit
• make_array and ts_generator functions to quickly prepare input/output for keras time series models
• new loss functions: loss_quantile, loss_tweedie and loss_negative_log_likelihood

Usage

# Dataset
library(m5)

# Neural Networks
library(aion)
library(keras)

# Data wrangling
library(dplyr, warn.conflicts=FALSE)
library(data.table, warn.conflicts=FALSE)
library(recipes, warn.conflicts=FALSE)

# ==========================================================================
#                          PREPARING THE DATA
# ==========================================================================

train <- tiny_m5[date < '2016-01-01']
test  <- tiny_m5[date >= '2016-01-01']

m5_recipe <-
  recipe(value ~ ., data=train) %>%
  step_mutate(item_id_idx=item_id, store_id_idx=store_id) %>%
  step_integer(item_id_idx, store_id_idx,
               wday, month,
               event_name_1, event_type_1,
               event_name_2, event_type_2,
               zero_based=TRUE) %>%
  step_naomit(all_predictors()) %>%
  prep()

train <- bake(m5_recipe, train)
test  <- bake(m5_recipe, test)

TARGET      <- 'value'
STATIC      <- c('item_id_idx', 'store_id_idx')
CATEGORICAL <- c('event_name_1', 'event_type_1', STATIC)
NUMERIC     <- c('sell_price', 'sell_price')
KEY         <- c('item_id', 'store_id')
INDEX       <- 'date'
LOOKBACK    <- 28
HORIZON     <- 14
STRIDE      <- LOOKBACK
BATCH_SIZE  <- 32

# ==========================================================================
#                          CREATING GENERATORS
# ==========================================================================

c(train_generator, train_steps) %<-%
    ts_generator(
        data        = train,
        key         = KEY,
        index       = INDEX,
        lookback    = LOOKBACK,
        horizon     = HORIZON,
        stride      = STRIDE,
        target      = TARGET,
        static      = STATIC,
        categorical = CATEGORICAL,
        numeric     = NUMERIC,
        batch_size  = BATCH_SIZE    
  )

c(test_generator, test_steps)  %<-%
    ts_generator(
        data = test,
        key = KEY,
        index = INDEX,
        lookback = LOOKBACK,
        horizon = HORIZON,
        stride = STRIDE,
        target=TARGET,
        static=STATIC,
        categorical=CATEGORICAL,
        numeric=NUMERIC
    )